a technical review of urban land use--transportation models as tools for eva
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7/31/2019 A Technical Review of Urban Land Use--Transportation Models as Tools for Eva
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A Technical Review of Urban Land Use--Transportation Models as Tools for Evaluating Vehicle Travel Reduction Strategies
ABOUT RITA | CONTACT US | PRESS ROOM | CAREERS | SITE MAP
A Technical Review of Urban Land Use--Transportation Models as Tools for Evaluating Vehicle Travel Reduction Strategies
ORNL-6881
A Technical Review of Urban Land Use-Transportation
Models as Tools for Evaluating Vehicle Travel
Reduction Strategies
Frank Southworth
Center for Transportation Analysis
Energy Division
July 1995
Prepared for
the Office of Environmental Analysis
and Sustainable Development
U. S. Department of Energy
Prepared by
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7/31/2019 A Technical Review of Urban Land Use--Transportation Models as Tools for Eva
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A Technical Review of Urban Land Use--Transportation Models as Tools for Evaluating Vehicle Travel Reduction Strategies
OAK RIDGE NATIONAL LABORATORY
Oak Ridge, Tennessee 37831
managed by
LOCKHEED MARTIN ENERGY SYSTEMS, INC.
for the
U. S. DEPARTMENT OF ENERGY
under contract DE-AC05-84OR21400
CONTENTS
Page
FIGURES v
EXECUTIVE SUMMARY vii
1. INTRODUCTION 1
1.1 URBAN TRAVEL GROWTH: WHY THE CONCERN? 1
1.2 PURPOSE OF THIS REVIEW 2
1.3 OVERVIEW OF THE TECHNICAL CHALLENGE 3
1.3.1 Conceptual Issues 3
1.3.2 Practical Issues 8
1.4 ORGANIZATION OF THE REVIEW AND MAJOR
CONCLUSIONS 10
2. INTEGRATED URBAN LAND USECTRANSPORTATION
MODELS 13
2.1 OVERVIEW 13
2.2 SURVEY OF EMPIRICALLY APPLIED MODELS 15
2.2.1 Survey of the Literature 15
2.2.2 Nature of Model Applications 19
2.3 MODELING THE URBAN TRANSPORTATION SYSTEM 20
2.4 LINKING TRANSPORTATION AND URBAN LAND USE
MODELS 272.4.1 The Lowry Model and Related Developments 30
2.4.1.1 Background 30
2.4.1.2 DRAM, EMPAL, and ITLUP 30
2.4.2 Normative Planning and Related
Mathematical Programming Developments 33
2.4.2.1 Background 33
2.4.2.2 The POLIS Model 35
2.4.3 Multisectoral Spatial Modeling Using
Input-Output Frameworks 41
2.4.3.1 Background 41
2.4.3.2 The MEPLAN Model 42
2.4.4 Contributions from Urban Economics 46
2.4.4.1 Background 46
2.4.4.2 Kim's Chicago Model 46
2.4.5 Uses of Micro-Analytic Simulation 50
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2.4.5.1 Background 50
2.4.5.2 The MASTER Model 51
2.5 APPROACHES TO URBAN DYNAMICS 53
2.5.1 Background 53
2.5.2 The Dortmund Model 55
iii
3. USING INTEGRATED MODELS IN POLICY ANALYSIS: AN
ASSESSMENT 593.1 INTRODUCTION 59
3.2 MODEL VALIDATION ISSUES 60
3.3 THEORETICAL ISSUES: TOWARDS MORE REALISTIC
MODELS 63
3.3.1 Household Travel Mobility Modeling 63
3.3.1.1 Criticisms of The Traditional
Transportation Planning Model 63
3.3.1.2 Implications for Modeling Travel
Reduction Strategies 65
3.3.1.3 Some Recent Developments in
Travel Demand Modeling 66
3.3.2 Urban Goods Movement Modeling 70
3.3.3 Modeling Transportation's Continued Role
in Urban Development 72
3.3.3.1 Transportation Infrastructure
Investment Impacts 72
3.3.3.2 Spatial Agglomeration of
Activities 74
3.3.4 Simulation of Urban Dynamics 77
3.4 PRACTICAL ISSUES: TOWARDS MORE USABLE MODELS 80
REFERENCES 83
FIGURES
Page
1.Complexity of Functional Linkages in Urban System Dynamics 7
2.Integrated Modeling: General Schematic Flow Chart 14
3.Traditional Four-Step Urban Transportation Planning Model 21
4.Simple Two-Route, Two-Link Congested Traffic Assignment 26
5.Integrated Urban Modeling Showing Typical Submodels 29
6.Multi-Period,Recursive Simulation of Urban System Dynamics 54
v
EXECUTIVE SUMMARY
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The continued growth of highway traffic in the United States
has led to unwanted urban traffic congestion as well as to
noticeable urban air quality problems. These problems include
emissions covered by the 1990 Clean Air Act Amendments
(CAAA) and 1991 Intermodal Surface Transportation Efficiency
Act (ISTEA), as well as carbon dioxide and related Agreenhouse
gas@ emissions. Urban travel also creates a major demand for
imported oil. Therefore, for economic as well as environmental
reasons, transportation planning agencies at both the state and
metropolitan area level are focussing a good deal of attentionon urban travel reduction policies. Much discussed policy
instruments include those that encourage fewer trip starts,
shortertrip distances, shifts to higher-occupancy vehicles or
tononvehicular modes, and shifts in the timing of trips from
themore to the less congested periods of the day or week. Some
analysts have concluded that in order to bring about
sustainablereductions in urban traffic volumes, significant
changes will benecessary in the way our households and
businesses engage indaily travel. Such changes are likely to
involve changes in theways we organize and use
traffic-generating andBattracting landwithin our urban areas.
The purpose of this review is to evaluatethe ability of current
analytic methods and models to supportboth the evaluation and
possibly the design of such vehicle travelreduction strategies,
including those strategies involving the reorganization and use
of urban land.
The review is organized into three sections. Section 1
describes the nature of the problem we are trying to model,
Section 2 reviews the state of the art in operational urban
landuseBtransportation simulation models, and Section 3
provides acritical assessment of such models as useful urban
transportationplanning tools. A number of areas are identified
where furthermodel development or testing is required. The
following is asynopsis of each section of the review.
Section 1 of the review describes the considerable technical
difficulties associated with identifying the causes and
directionsof urban traffic growth. It is concluded that to be
effective,transportation planning needs to bring together an
understandingof (1) how the transportation sector operates, (2)
how traffic-generating and attracting land is developed, (3)
how other technologies affect the demands for travel, (4) how
moderncompanies make their siting and site relocation
decisions, and (5)how the modern industrial lifestyles of
today's households affect,and are in turn affected by, each of
the above. Besides the complex conceptual issues involved,
challenging practical issues result from the need to handle
large amounts of spatially explicit data, and the need to
consider a wide range of possible, and sometimes competing
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transportation control measures (TCM). Significant,
sustainable, and socially acceptable travel reduction
strategies will require careful multiyear land use planning.
Given the typical time lag between the opening of a major
transportation infrastructure or service and the
vii
subsequent land use response, interest is focussed in this
reviewon models capable of simulating policy impacts anywherefrom15 to 50 years into the future.
Section 2 reviews the current status of operational land
use-transportation planning models, and in particular the
development of Aintegrated@ urban analysis models. A listing
ofthe most commonly referenced models is provided. The key
theoretical and operational developments of the past 30 years
arediscussed, using the mathematical details from selected
modelingsystems to illustrate the range of approaches now
available forsimulating urban travel patterns and their
multiyear impacts. Taken as a set, current models have managed
to combine the entropy maximization and locational
accessibility premises that are the basis of spatial
interaction theory with economically rational notions of
utility maximization and consumer choice.From the urban
economics literature they have taken the idea of equilibration
between transportation demand and supply and linked it to a
residential market clearing process.Methodologically, they make
use of nonlinear mathematical programming methods,
interregional input-output methods, and the latest developments
in econometrics and microsimulation to model jointly the
demands for travel, residences, employment, services, and urban
land. The more comprehensive models also simulate demographic
changes in the urban population as well changes in physical
stocks other than transportation infrastructure, includingmodels of the aging and renewal process associated with the
urban housing market.
The key trait these models have in common is their ability
to feed back the expected results of adding new transportation
infrastructure or services, computed within a transportation
submodel, to a travel cost sensitive land use submodel. They
simulate urban dynamics by iterating the simulated urban system
through a series of discrete time intervals. Here the level of
sophistication varies considerably across models: from a simple
one-shot,30-year forecasting process, to recursive formulations
which move the urban system forward in time through a series of
successively updated, 1-to 5-year intervals. They model these
events using an extensive database, usually resulting in the
allocation of traffic volumes and speeds over detailed
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link-node representations of multimodal urban transportation
networks. They have been used in a number of different
countries to simulate a range of travel reduction strategies,
including fuel and road pricing policies, the spatial
reallocation of traffic-generating land uses, and the
introduction of new highways and transit services.
However, despite advancing in a number of theoretical and
practical directions since Lowry's 1964 "Model of Metropolis,"
these models are only now finding their way into U.S. practice.
Past reticence to employ them has stemmed in part from theiranalytic complexity, in part from their significant data
requirements and similarly significant demands on
computational resources. While today's desktop computers can
now provide much of the computing power required, the other
issues remain unresolved. Spurred on by the demands placed on
metropolitan planners by the CAAA and supporting ISTEA
legislation, these models are now receiving renewed scrutiny.
At the same time, recent
viii
empirical and theoretical developments suggest that current
models may need to be either adapted or replaced if realistic
simulations of traveler responses to travel-reduction
strategies are to be forthcoming. Here a difficulty facing
model assessment is the limited information available from
model validation exercises, a process exacerbated by the
extended time frames required to capture the true effects on
travel of the moresignificant land use changes.
Section 3 considers a number of frequently voiced criticisms
of currently operational models and recasts these perceived
weaknesses as candidate areas for further research. Many of
these criticisms are linked to continued use of thetraditional four-step urban transportation planning model, and
in particular,the persistence of a single-destination, single
trip-purpose-based approach to travel generation. There is a
widely recognized need to develop more effective ways to
capture nontraditional travel reduction options, such as
telecommuting and teleshopping, alternatively fueled but
perhaps limited-range vehicles, and nontraditional work weeks.
Improved "travel activity analysis" models under development
include the modeling of multidestination, multipurpose trip
chains; the simulation of private vehicle use by different
household members and types of households; and the simulation
of daily travel schedules which recognize the growing number of
noncommute, non-peak period trips which are taking place.
Similarly, our treatment of the urban goods movement process
lacks any underlying behavioral rationale and needs to be tied
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to a more comprehensive understanding of company logistics
planning. Some recent developments in both personal and goods
movement modelingare referenced as useful starting points for
subsequent analysis.
Needed improvements to the land use modeling process are
also discussed. In particular, and despite the frequently
referenced polycentric nature of urban growth over the course
ofthis century, there has been a failure to come to terms with
the causal mechanisms underlying intraurban, notably suburban,
center growth. The urban economics literature,while extensive,has so far contributed little in the way of operationally
implementable theories of urban development. Among other
barriers to understanding, outmoded notions of what constitutes
"basic" and "nonbasic"employment activity make it difficult to
identify the underlying causes of commercial and industrial
business location decisions. A reassessment of this
traditionaldistinction, already evident in a number of recent
modelingefforts, needs to be pursued in a more comprehensive
manner.
A second area of land use planning warranting further study
is a more normative, or design-based, approach to urban
activity center planning. This includes approaches centered on
transit-oriented development and pedestrian-and cycle-oriented
land usearrangements.
ix
Third, a gradual move towards more behaviorally realistic,
truly dynamical modeling approaches is discussed, based on
differential or difference equation forms and supported by
longitudinal data such as multiwave panel analysis of
empirically validated travel behaviors. If such dynamical
analysis can be combined with a better understanding of why andhow urban centers form, and how designs of mixed use activity
centers influence household and business travel patterns, we
would have the basis for more realistic, and perhaps eventually
prescriptive,travel activity pattern simulations.
Finally, these urban simulation models need to be placed
within today's highly interactive software environments. We
need to produce not only policy-relevant, but also
policy-usable analysis tools. Urban planning ought to be a
highly interactive, consensus-building process. Black box
models should be neither acceptable nor necessary. Models
should be placed withinspatially explicit decision support aids
taking advantage of the latest geographic information systems
and relational database technology to open up the planning
process to well-informed local and regional planners.
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Ultimately, urban planning comes down to compromise and common
sense. Yet we would be taking considerable risk, as we have
often been forced to do in the past, if we were to assume away
the complexity associated with multiyear planning by selecting
travel policies based largely on professional intuition.
Simulation models are necessary if we are to understand the
consequences of trying to control future traffic growth, and a
degree of complexity in model design cannot be avoided.
x
1. INTRODUCTION
1.1 URBAN TRAVEL GROWTH: WHY THE CONCERN?
Highway transportation today accounts for some 22% of the
nation's annual energy consumption: 97% of it in the form of
petroleum-based fuels (Davis, 1994). The century has been one
of steadily growing demand for vehicular travel. Between 1970
and 1990 total vehicle miles of highway travel within the
United States grew at an average annual rate of 3.2% (Davis,
1994, Table 3.2). While some reduction in this rate of growthmay result from a saturation in vehicle ownership and license
holding, many experts expect urban travel to continue to
increase as a result of (1) significant population gains within
our largest cities (Downs, 1992), (2) a generally growing
interest in discretionary forms of nonwork travel (see Hu and
Young, 1994), and (3) our continued failure to develop
alternatives to low-occupancy vehicle use (Johnson, 1993).
One evident impact of this traffic growth has been urban
pollution. Mobile source emissions from the highway
transportation sector alone are estimated to account for some
70% of our society=s carbon monoxide generation, 39% of itsnitrogen dioxide, 30% of emissions of VOCs , and 28% of its
small particulate matter (PM-10) generation, along with
significant contributions also to nitrogen oxide and sulphur
dioxide emissions (Curran et al., 1992). Nor are these the
only emissions of interest. Increased atmospheric accumulations of
carbon dioxide and related tracegasesCnotably ozone, nitrous
oxide, methane, and chloroflourocarbonsCare today considered
by many scientists to be contributing to a "greenhouse effect,"
in which the level of heat retained within the planet's
atmosphere is causing global warming of the earth's surface.
As such concerns have passed from the scientific community into
wider public notice, interest in the amount of carbon dioxide
(CO2) resulting from motor vehicle use has begun to surface
with some regularity. It has been estimated that the
consumption of energy within the transportation sector
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contributes some 32% of the nation's emissions of carbon
dioxide (Hillsman and Southworth,1990).
In addition to these now often-discussed "direct," or
vehicle miles of travel-based, estimates of fuel consumption
and emissions production, DeLuchi, Johnson, and Sperling (1987)
identified five additional, indirect sources of greenhouse
gases which result from the consumption of highway and other
transportation fuels. These are (1) end-use combustion of
fuels, including trucking of liquid transportation fuels to
retail outlets; (2) combustion of fuel in pipeline compressors
and pumps, and in barges and trains during wholesale
transmission of fuels to the distributor; (3) CO2 formed by the
chemical reactions of fuel synthesis; (4) CO2 formed by the use
of process energy in fuel
Volatile organic compounds, which along with nitrogen oxides
are precursors of ozone (o3)
1
2
production plants; and (5) combustion of fuel in the initial
extraction, preparation, and transportation of raw fuel
feedstock. To these sources we also need to add the emissions
generated in those processes used to build and maintain our
transportation infrastructure and its operating components,
including our roads, bridges, and vehicle and vehicle parts
manufacturing plants, and in the manufacture of the vehicles
themselves.
Air quality and fuel consumption are not the only publicpolicy issues, of course. Activities associated with the
transportation sector now cover a significant percentage of the
land in use within our cities. With infilling of development
between the major highway arteries that were the original
facilitators, if not progenitors of that growth, the need for
additional centers of activity besides the CBD emerged. The
result has been multicentered urban development in most large
cities and a consequent increase in suburb-to-suburb trips.
Many of these suburban centers are now suffering from their own
versions of traffic congestion and the losses of personal and
employee time that entails (see Orksi, 1985;JHK and Associates,
1989; Garreau, 1991; Southworth and Jones, 1995). And what
the ubiquitous automobile has done for personal mobility the
truck has done for the intraurban movement of goods, leading to
a growing number of instances of mixed truck-automobile
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interaction, which raising additional issues of travel safety
as well as traffic congestion. A question now being asked is
where our overcongested cities will go from here. How can we
most cost-effectively deal with traffic growth and traffic
congestion in a socially as well as environmentally sound
manner?
1.2 PURPOSE OF THIS REVIEW
Spurred on by this interest, this review focusses on the
extent to which current theories and supporting methodologies
are sufficiently developed to be used (a) to help urban
planners assess the impacts of transportation plans and
policies which support the evolution of more energy-efficient
and less polluted cities, and (b) to aid in the design of
specific travel-reduction strategies.
Given the complexity of the subject, methods are here
synonymous with models: conceptual, mathematical, and for
practical purposes, computer-based. For almost four decades
now, we have been using computer-based urban transportation
planning models to improve our assessment of current travel
activity patterns and to predict future transportationinfrastructure needs in support of steadily growing automobile
and truck traffic. For the purposes of longer-range
forecasting, in the 15- to 50-year range, such transportation
planning models need to be tied to a broader-based land use
plan for the same region. Often, such land use plans are
themselves the result, at least in part, of a modeling
exercise. As our cities have grown, the relative advantage of
locations within them has changed because of the growing
demand for goods and services, the buildup in traffic
congestion, and the further development of the transportation
system in response to both of these forces. That is, the ease
or cost of travel between locations in turn
3
contributes to the economic vitality of specific business
enterprises, as well as to the desirability of specific
residential locations. With the passage of time, changes in
transportation costs may in turn cause a change in land use.
Linking transportation and land use planning exercises is
therefore a natural step in both the physical and the
subsequent economic planning process. The current status of
Aintegrated@ urban land use-transportation models is the
central topic of this review.
Of particular interest is the ability of such integrated
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models to provide useful inputs to the selection of travel-
reduction strategies that will result in a net reduction in
aggregate fuel use and emissions. Such reductions are usually
thought of as resulting from one or more of the following five
outcomes:
1. a reduction in the number of trip starts;
2. a reduction in the length of individual trips, through
changes in destination;
3. a shift to either nonvehicular or higher-occupancy
modes of travel; and/or
4. a reduction in the amount of travel during the
congested, or "peak," commuting periods
5. a reduction in trip length and/or traffic congestion,
through changes in route.
In this review the strategies we are most interested in
are those that can maintain such travel reductions over a
period of years, and thus improve urban lifestyles
collectively as well as individually. For this reason also,
the sort of planning orizons we are interested in, and those
also best suited to the types of models reviewed below, cover
the 15- to 50-year time frame, although many of the processes
modeled may express themselves (and be simulated to do so) with
much shorter cycles. The emphasis of the review, the readershould note, is on applicability of current methods, and not on
the applicability of specific travel-reduction strategies per
se.
1.3 OVERVIEW OF THE TECHNICAL CHALLENGE
1.3.1 Conceptual Issues
It is important first of all to note the complexity of the
relationships we are seeking to simulate. The role
transportation plays in the multiyear development of urbansystems remains far from clear. This is due in part to the
often long lag times between the introduction of a new highway,
rail line, or travelterminal and the subsequent effects on
surrounding businesses and residents. To date, our ability to
track such changes in a comprehensive manner has been a cost we
have generally not been willing to accept. What is clear is
that many different factors work simultaneously to shape our
cities. While the root causes of travel growth are found in
the development of urban land-what it is used for and how
intensively it is used-we are currently much less certain about
the subsequent effects of transportation system changes on land
use, and hence, in turn,
4
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on longer-run travel patterns (Giuliano, 1989; TRB, 1991;
Kitamura, 1994). Public policies intended to produce
sustainable forms of energy-efficient and environmentally
acceptable travel must encompass a better understanding of the
broader topic of urban land use, and in particular, of the way
transportation and other forms of urban land use feed back on
one another. Demonstrating the difficulties we face in
unravelling causes and effects, Zimmerman, West and Kozlowski
(1974) separated traffic which occurs on a new or newly
expanded highway into the following classes quoted by Kitamura,
1994):
. existing traffic,
. natural-growth traffic-due to traffic arising from
demographic or socioeconomic changes,
. diverted traffic-traffic from other streets and
highways,
. transferred traffic-traffic from other travel modes,
. shifted traffic-traffic going to new destinations,
. induced traffic-"new" trips encouraged by the
presence of the new highway, and
. development traffic-traffic generated by land-use
changes.
In recent years the opening or widening of a major stretch
of urban highway has frequently resulted in rapid traffic
growth. The road seems to fill up in a surprisingly short
period of time, then settles down to a new and generally higher
level of daily use. In areas where the demand for additional
road space has been building for some time, much of this new or
induced trip making by businesses or households may be an
expression of the latent demand for greater access to
opportunities which already existed within the system. Recent
evidence suggests that within U.S. cities over one million in
population, such latent demand may represent as much as 13% of
any new travel induced by a highway capacity expansion (Rathiet al., 1991).
In addition to the above effects, temporal changes in trip
making may also influence the picture. Greater ease of travel
may induce some freight as well as personal travel to shift to
the now less congested highway, possibly into the peak period of
use. Many cities in recent years have experienced a temporal
spreading of such peak congestion periods, in what is a
collective expression of trip departure timing adjustments in
the face of congestion delays. Certainly, experts are often at
odds on the likely effects of adding new highway capacity in a
given situation (see Deakin, 1991).
As with highways, the full impacts of introducing new
transit infrastructure also remain far from clear. Over the
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past three decades a number of studies of the effects of
introducing new or improved rail service, and to a lesser
extent new bus or trolley lines, have been carried out.
Armstrong (1994) summarizes the results of the best-known U.S.
studies (see also Dehghani and Harvey, 1994; Hunt, McMillan,
and Abraham,1994). The
5
findings appear to support the notion that property values
suffer in the immediate vicinity of intraurban rail stations,
and possibly (though less conclusively) along rights-of-way but
that rents may increase on average within the communities
supporting heavy rail or commuter rail transitstations.
However, results are far from consistent across studies,
except that any tendencies toward more compact urban growth or
higher urban densities appear to be offset by larger forces
towards urban decentralization (Deakin, 1991; Giuliano, 1989).
Giuliano (1986a), Mackett (1994), and Pisarski (1994) each
briefly review a number of past highway and transit investment
programs and their attributed impacts on subsequent urban
development and land prices. While the transportation-land use
feedback effects do often occur, current evidence andunderstanding does little to clarify the situation for the next study
to be carried out. What is clear is the need to use models
which recognize the above complexities if we are to unravel the
conundrum posed by transportation infrastructure-initiated
urban development.
In trying to model not only the more immediate mode and
route shifts but also the longer-term relationships between
transportation and other forms of urban land use (notably
destination shifted, induced, and development traffic) we are
dealing with both a large number and a wide variety of activity
types, decision makers, and underlying motives for action.While urban residents have chosen in growing numbers to move
outward from the city centers in search of more space at lower
rents, most commercial and industrial land users still seek the
economies of scale associated with spatial proximity to similar
and complementary employment activities. With the onset of the
information society, a third important trend is the emergence of
locationally indifferent, or Afootloose,@ service and
information- based companies which are no longer tied to the
location of key resource inputs or local markets for their
products. We therefore have at least three very different
types of locational activity operating within our urban areas.
Making the situation more complex, the locational decisions
within each of these residential and employment activity
sectors ultimately impact each other. Where workers live
determines the available labor pool, and where residents work
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affects their choice of residence. Choice of residence in turn
affects the size of the consumer market for service and retail
products.
Further complicating the situation, the very urban stage
on which we are trying to apply our models has been shifting
quite rapidly of late. As a society we are undergoing some
significant changes not only in the way we travel but also
in the way we communicate and indeed live with each other. New
urban lifestyles are emerging as new forms of urban household
take shape (see Putman, 1994, for a discussion related to
recent land use modeling experience). Similarly, the role to
be played in future cities by the ongoing telecommunications
revolution is far from clear (US DOT, 1993; Greene, Hillsman,
and Wolfe 1994). To be useful planning tools our models must
be capable of incorporating different assumptions about the
effects of today's emerging travel and communications
technologies on the ways we interact with one another, both
within and between future cities.
6
As a minimum then, effective transportation planningmust bring together an understanding of (1) how the
transportation sector operates, (2) how traffic-generating and-
attracting land is developed, (3) how other technologies affect
the demands for travel, (4) how modern companies make their
siting and site relocation decisions, and (5) how the modern
industrial lifestyles of today's households affect, and are in
turn affected by, each of the above.
Figure 1 shows the sort of complexity we are trying to
come to terms with, if we take up the challenge of trying to
simulate, in any reasonable detail, the multiyear impacts of
urban transportation plans. Transportation demand and supplyconsiderations are shown at the center of a readily and rapidly
expandable series of interconnected causes and effects. Demands
for new and better transportation services are shown as
resulting from changes in the utilization of urban land. The
travel cost changes which result from providing new
transportation services cause activity pattern shifts which in
turn affect the local economy (i.e., the revenue generated by
the purchase of goods and services at specific sites). These
changes in turn affect local employment, which in turn affects
local demographics. Changes in employment and population
affect demand for services (of all kinds) which can either
create new businesses or cause businesses to close down with
loss of their competitive advantage. These sociodemographic
changes also affect local housing prices and eventually the
need for new housing starts. With new business ventures and new
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residential neighborhoods come new demands for travelCand the
cycle begins again.
Shown at various locations within Figure 1 is the
potential for federal, state, metropolitan and local
governments to influence urban activity patternsCnotably
through transportation service pricing and capacity control,
through urban land utilization and labor supporting policies,
and through environmental legislation. Within the United
States this includes the use of private/public sector
partnerships in the development of local services. Also shown
in Figure 1 are a number of "other factors" involved in both
the transportation and land use decision-making processes of
each of the actors involved. These include the regionwide and
nationwide adoption of cost, time, and labor saving
technologies, including the advances in automotive engine
design and alternative fuels technology which have been the key
sources of travel-related fuel and emissions reductions to
date.
Adding the need to understand the energy related
environmental impacts of particular transportation system
developments further complicates the matter. Shape, size,
density of development, and the spatial dispersion ofactivities have all been found to influence transportation
energy requirements. However, even highly abstract studies
7
Click HERE for graphic.
8
energy-efficient land use patterns quickly throw up
complexities which cloud interpretation of results (see Owens,
1989), while past empirically based modeling efforts leaveconsiderable room for uncertainty of cause and effect (see
Southworth and Jones, 1995). The actual outcome for fuel use,
and in particular for emissions, of specific spatial
arrangements of activities is, again, far from clear. What
such studies demonstrate is the underlying complexity of the
issues involved in the development of energy efficient and
environmentally clean cities. As Pisarski puts it (TRB, 1991),
"The attempt to express, much less understand, the nature of
the relationships inherent in transportation, urban form and
the environment is a great challenge. Analysis can be
overwhelmed by the inextricable linkages between them, each
shaping, and shaped by, the others."
1.3.2 Practical Issues
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The above is a summary of the many conceptual issues involved
in urban land use-transportation interactions over time.
On a more pragmatic note, simulating the real behavior of a
complex urban system also requires the manipulation of
substantial detail, what Harris (1983) has called the Acentral
dilemma@ with regard to our design and construction of policy
useful urban planning models. This detail is required because
metropolitan transportation plans are by their nature spatially
explicit. They are involved at the fully urban scale with
planning for hundreds of thousands of travelers and hundreds of
firms. Depending on the model used, developing such plans
requires the availability of substantial amounts of spatially
referenced data, including socioeconomic-demographic data,
network structure data, travel cost data, and possibly housing,
commerical, and industrial stock data as well as data on land
rents and other factor prices. Indeed, past models have to a
significant degree been used to apply theory to fill gaps in
existing travel survey and land use inventory data.
As a corollary to this situation, the possible policy actions
are also very numerous, and quite varied. Consequently, the
attempt to configure policy sets out of combinations of these
actions can quickly lead to a combinatorial explosion. The
necessary detail required to accommodate such policy analyseswithin suitably comprehensive model-based tools can, if not
cleverly controlled, become quite staggering.
Table 1 lists many commonly cited transportation control
measures (TCMs) as they correlate with the five general types
of travel-reduction strategies listed in Section 1.2 (see, for
example, Boyce et al., 1981; Ferguson, 1990; Euritt et al.,
1994). A concern clearly evident within recent literature is
that many of these TCMs are only stopgap measures or
short-term solutions to the larger questions of how our cities
ought to evolve (see Giuliano, 1992; Bae, 1993). The search
for cleaner and more fuel-efficient futures may require moreradical assessments of our current position. Among the TCMs
listed in Table 1 the most promising for sustainable reductions
in travel appear to be associated with the following:
9
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(1) more efficient urban land use arrangements;
(2) different forms of travel pricing policies (road tolls,
parking charges, high-
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p g g
occupancy-vehicle fare subsidies, fuel and/or emissions
taxes);
(3) how best to use the latest developments in low-cost
information processing and telecommunications
technology as both a substitute for and a complement to
the movement of people and goods.
Within this review, considerations 2 and 3 are subsumed
under the notion of a more comprehensive view of Aintegrated
land use-transportation modeling@ which encompasses responses
to pricing policies and to real-time information systems as
part a broader, multiyear analysis of urban lifestyles and
business practices. Writing more generally about systems
modeling for public policy, Simon (1990, p. 7) sums up the
significant technical challenge we face as follows: "We must
separate what is essential from what is dispensable in order
to capture in our models a simplified picture of reality
which, nevertheless, will allow us to make the inferences that
are important to our goals."
A review of urban land use-transportation modeling can
therefore be viewed as an assessment of how well we have
managed to build such a picture. This is the perspectivewithin which the rest of the review is framed. To get a proper
picture we will certainly need to model it. How fine-grained a
picture we need to create in order to produce policy-sensitive
and sensible models remains a difficult question, but is
arguably the most important technical question we need to
answer.
Finally, it is important to remember that urban planning is
typically a multijurisdictional affair, involving local
metropolitan, as well asCwhere transport is concernedCregional
and federal decision-making. Effective modeling must take
place within such institutional arrangements and is subject,like the rest of the planning process, to the reigning
institutional priorities. This suggests the development of
flexible and highly interactive decision support tools which
intimately involve planners at all levels in the policy
analysis process.
1.4 ORGANIZATION OF THE REVIEW AND MAJOR CONCLUSIONS
The rest of this review is organized as follows. Section 2
describes the current state of the art in operational,
integrated urban land use-transportation modeling. This
includes a review of the major theoretical and methodological
underpinnings of both the transportation and land use
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p g g
components of such modeling systems, as well as a brief
summary of current best practice. Section 3 summarizes the
strengths and weaknesses of existing approaches as reflected in
recent commentaries within the literature. While real
11
progress has been made over the past three decades, and many
advances have passed from one modeling system to another, a
number of important weaknesses remain. Just how significant
current weaknesses are for policy analysis is currently
difficult to assess, given relatively limited application of
the majority of these modeling systems in actual planning
practice. Reasons for the limited application of these
models to date are discussed. As detailed forecasting tools,
the current models are entirely acceptable, even in the hands
experienced users. However, the nonintuitive results which
such model-based exercises regularly throw up suggest that they
represent a necessary component of future planning practice.
To better replicate traveler responses, however, more
behaviorally explicit models appear to be necessary if we are
to achieve greater realism.
We also need to recognize that such models, in their
software manifestations, are most useful as aids to scenario
generation and plan robustness testing, rather than as detailed
forecasting tools. By taking advantage of today's low-cost and
high-speed computers, we have the opportunity to move into a
new generation of urban transportation planning methods which
place planners within a highly interactive, multimedia-based
approach to the development of strategically focused and
incrementally adaptable urban transportation plans. Here, the
strategic role of models is to look for the errors that may be
associated with planner intuition, especially the errors which
can result from single or limited objective and perhaps myopicpolicymaking.
2. INTEGRATED URBAN LAND USECTRANSPORTATION MODELS
2.1 OVERVIEW
This section of the report is devoted to a review of
operational integrated urban land use-transportationCmodels,
that is, models which have been empirically applied in either a
research or actual planning context within the past decade. The
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term Aintegrated@ implies a feedback mechanism of the type
shown in Fig. 2, between the transportation system and the rest
of the urban land use system. Here the "land use' system
supplies the transportation system with estimates of the
location and volume of travel generators. "Land use" is a
general term here, covering both the types and intensities of
activities taking place at specific urban sites as well as the
physical area of land and any built structures used in support
of such activities. This involves modeling the demand for
employment, residential, shopping, and other activities at
different sites, and then translating and possibly constraining
these demands on the basis of appropriate physical or
artificial (i.e., planner-imposed) land utilization rates. The
more ambitious models also include the simulation of housing
stocks and floor space requirements for industrial buildings.
Within some models this also means simulation of pricing
effects on, in particular, residential choice. A further
extension in a limited number of modeling systems is a linked
simulation of demographic change, allowing the urban area's
population to evolve along with the evolution of the physical
city within which it lives and works. Wegener (1994) refers to
these types of model as "integrated urban models," although
the interaction between transportation and other land uses
remains their key trait.
The spatial distributions of residents and workers are
assumed to create the major demands for travel which drive
development of the transportation system. The "transportation"
system in Fig. 2 represents both the physical infrastructure and
services provided by the different travel modes, either
separately or in combination, as well as these demands, now
translated into mode-specific vehicular and nonvehicular trips,
for either passenger or freight movement. This interplay
between travel demand and supply resolves itself within the
typical transportation model into a series of single-purpose
and single- destination trips which together form theon-the-road traffic volumes of interest to an environmental
analysis of fuel use and mobile source emissions.
The origin-to-destination travel costs resulting from this
interplay between transportation demand and supply can be fed
back into the residential and employment activity location
models, where they are used to allocate the area's residents
and workers to specific urban zones within the land use model.
This allows transportation system
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15
changes to affect land utilization, which in turn feeds back
its effects in the form of new levels (and locations) of
here plays a central role in all currently operational models.
As an integral component of such accessibility, travel cost
changes become part of the mechanism used to reallocate labor,
residents, retail and service activities, and when modeled,
freight flows between spatially separated land uses.
In terms of an urban dynamic, most models employ static-
recursive approaches to multiyear forecasting or (more
realistically) scenario generation. That is, cross-sectional
representations of the urban system are moved forward through a
series of discrete time intervals. However, both the
operational details and level of sophistication imposed on this
dynamic vary considerably across existing models. This is the
topic for Sect.2.5 below. A common planning horizon for such a
single- time-period forecast is 5 years, although intervals
from 1 year to as many as 30 years have been used. Forecasting
further into the future, an obviously risky business, is
accomplished in the more advanced modeling systems by iterating theland use and transportation subsystems through a series of
discrete time intervals. In an effort to keep transportation
and other urban land uses in some kind of synchronization, both
lagged and marginally incremental methods are used to update
and to control for selected variables as part of this process.
Figure 2 also shows the location of three types of public
policy instruments commonly used to simulate the effects of
significant travel reduction strategies: (1) land use controls,
(2) fuel pricing policies, and (3) those transportation control
measures which impact directly the capacity and level of service
of the specific transportation modes.
2.2 SURVEY OF EMPIRICALLY APPLIED MODELS
2.2.1 Survey of the Literature
Table 2 lists the better-known and documented
operational models, along with some of their applications to
specific urbanized area studies. The table draws heavily on
the models reported by the International Study Group on Land
Use- Transportation Interaction (ISGLUTI) (Webster, Bly and
Paulley, 1988), on the survey of available models by Cambridge
Systematics and The Hague Consulting Group (1991), and on the
Coordinated through the British Transport and Road Research
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Laboratory . tje OSGLUTI effort carried out comparisons of nine
different land use-transportations models using data from cities
in seven different developed countries(Annerstookt in
Netherlands; Tokyo an Osaka in Japan; Dortmund in Germany;
Leeds in the England; Bilbao in Spain; Uppsala in Sweden; and
Melborne in Australia). Subsequent work has extended these
model comparisons to (a) the application of more than one
model to the same city (see Wegener, Mackett and Simmonds,1991)
and (b) the application of the same model to more than one city
(Mackett) 1991 b). This sort of coordination is now being
continued through the SIGI working group within the World
conference on transportation Research
16
Click HERE for graphic.
17
reviews by Berechman and Gordon (1986), Berechman and
Small (1988), Mackett (1985), Putman (1983, 1991), and
Wegener (1994, 1995b). These sources were supplemented by afurther literature search and through contacts with a number of
the field=s leading model developers.
Among the most recent round of empirically supported
U.S. studies of note are those for the Chicago area (Anas and
Duann, 1986; Boyce et al., 1992, 1993; Kim, 1989); for the
San Francisco Bay Area (Prastacos, 1986a,b; Caindec and
Prastacos, 1995); the Puget Sound Region of Washington State
(Watterson, 1993); and Portland, Oregon=s Land Use,
Transportation, and Air Quality (LUTRAQ) study (Cambridge
Systematics C Hague Consulting Group, 1991). The most
widespread application of a particular modeling approach in theUnited States comes out of the extensive model development and
calibration efforts of Putman and colleagues (see Putman 1983,
1991) , whose joint implementations of the Disaggregate
Residential Allocation Model (DRAM) and the Employment
Allocation Model (EMPAL) are currently used in some fourteen
of the largest U.S. metropolitan planning agencies (Putman,
1994). During the 1970s and 1980s Putman also developed the
Integrated Transportation Land Use Package (ITLUP), linking
DRAM and EMPAL with selected components of the traditional
four-step transportation planning model, containing submodels to
estimate trip distribution, modal choice,and traffic assignment.
References to past empirical applications of ITLUP include
studies in Kansas City, Washington, D.C., and Houston. The
LUTRAQ study also recommended use of an ITLUP-like
approach (Cambridge Systematics et al., 1992b). Recently
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DRAM and EMPAL have been linked to the TRANSPLAN suite
of transportation planning models in a 772-zone application to
the southern California region, centered on Los Angeles (Putman,
1994).
Building on the CATLAS model of combined residential
location, housing and mode choice, the modeling of non-work
travel choices and commerical real estate markets in the New
York region (the NYSIM model), and the modeling of
metropolitan housing market dynamics in a number of US cities
(the CHPMM model), Anas and colleagues have developed a
highly integrated economic model of transportation and land use
called METROSIM. METROSIM (Anas, 1994) consists of 7 sub-
models, providing analysis of a region's basic industry, non-
basic industry, residential and commercial real estate, vacant
land, households, commuting and non-commuting travel and
traffic assignment, within a single, jointly solved-for
structure, that is strongly oriented towards theoretically sound
and empirically workable economic relationships.
I n the United Kingdom notable efforts to develop land
use-transportation models are found in both the theoretical and
the empirical work begun by Wilson and colleagues at the
University of Leeds (see Wilson et al., 1977, 1981), and carriedon by Mackett at the University College, London. Mackett has
devoted considerable effort to building and calibrating both the
Leeds Integrated Land Use-Transportation modeling package
(LILT) (Mackett 1983, 1991a,b) and the MASTER
microsimulation-based modeling system (Mackett, 1990b).
Echenique and colleagues at the University of Cambridge, and
subsequently within the commercial sector, have been especially
energetic in developing
18
and applying the MEPLAN modeling system. Their work includes
planning applications for the city of Bilbao, Spain, and for the
Third World cities of Sao Paulo, Brazil (Echenique, 1985);
Caracas, Venezuela (Feo et al., 1975); and central Chile (de
la Barra et al., 1975). Hunt and Simmonds (1993) reference 22
different empirical applications of MEPLAN, including a recent
study for Naples, Italy (Hunt, 1994). A similar but now
separate modeling system, TRANUS, has also been applied to the
la Barra, 1989), as well as to more idealized simulations of
energy and urban form relationships (de la Barra and Rickaby,
1982; Rickaby, 1987, 1991). Johnston (1995) indicates that
TRANUS is currently being experimented with in in Sacramento, at
the University of California at Davis, where it is beingexamined in conjunction with the California Urban Futures Model,
or CUFM (see Landis, 1994).
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also report the existence of two commercially available, ITLUP-
like computer packages known as TRACKS and TRANSTEP,
with 51 reputedly different applications in Australia and the
Far East.
2.2.2 Nature of Model Applications
As a set, the models listed above have been empirical by
applied to a wide range of policy questions. While the initial
reasons for developing the various modeling approaches may
have differed, the ISGLUTI study found sufficient similarity
across nine of the models reported in Table 1 to carry out a set
of common tests. These tests covered the effects on travel
choices and land use arrangements from introducing changes in
the following variables:
. population,
. land use restrictions,
. employment location policies,
. the location of retail (shopping) facilities,
. the costs of travel,
. mode-specific travel speeds and network structure,
. the timing of transport investment, and
. general economic climate (economic recession,narrowed income distribution).
Moving into specific policy impact studies, Mackett
(1994) concludes that current models can be particularly useful
for analyzing either congestion reduction or energy reduction
strategies. (Also considered were safety, the environment,
social equity, quality of life, public expenditure and
privatization policies.) He lists the following commonly
available (if not always popular) public policy instruments as
being well suited to analysis with models which integrate
transportation planning decisions into a broader and longer-
range analysis of land use.
. restrictions of peripheral urban development,
. increases in the gasoline tax,
. increases in public transportation subsidies,
. increases in investments in public transportation
infrastructures,
. increases in transportation system (supply)
management,
. increases in transportation demand management, and
. introduction of road pricing schemes.
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In a research context a handful of past studies have also
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used such models to look specifically at alternative, if rather
abstract, energy-efficient urban futures (see Sharpe,1978, 1980,
1982; de la Barra and Rickaby, 1982; Rickaby, 1991; Roy and
Marquez, 1993). In the United States the 1990 Clean Air Act
Amendments and supporting legislation within the 1991
Intermodal Surface Transportation Efficiency Act have caused
recent practice to focus on using such models to forecast future
levels of urban air quality (see Putman, 1994, using the
DRAM/ITLUP modeling approach; and Watterson, 1993, using
modified versions of the DRAM and EMPAL models within the
Puget Sound Council of Governments model). The spatial as
well as temporal extent of such applications also varies, from
specific highway or transit corridor analyses to full-scale
urban area or complete transit network simulations.
2.3 MODELING THE URBAN TRANSPORTATION SYSTEM
Urban transportation modeling began in earnest in the
mid-1950s in the United States (see Weiner, 1992, for historical
developments). Since the 1960s most metropolitan areas have
used variants of the Urban Transportation Planning System
(UTPS) models shown in Fig. 3. This four-step, single-
destination, separable-purpose, daily trip-based approach hasdominated the transportation modeling literature. This includes
its use within the integrated land use-transportation models
listed above. It has been used to address a wide range of
issues covering the physical, economic, and ( in recent years)
energy and environmental impacts of major highway or rapid
transit investments. The approach is sequential in order to
avoid some very difficult multicollinearity problems found to
affect more direct estimation techniques. It is also meant to
be iterative in order to bring the transportation costs computed
within the trip distribution (= destination), modal choice, and
traffic assignment (routing) submodels down to a common set of
values.
In this system the urbanized area is first divided up into a
set of spatially contiguous traffic-generating and attracting
zones. For our largest cities this involves definition of
dozens, sometimes hundreds, of zones linked to highway and
transit networks containing hundreds, sometimes thousands, of
link and node records. The computational process can be started
with a simple all-or-nothing assignment of traffic to least-cost
interzonal travel paths. This can be done before any actual
trip volumes are Aloaded @ onto the network. Land use, when
modeled explicitly, comes into the process through its influence
on trip generation rates. Alternatively, daily trip frequencies
are estimated directly from zonally based population andemployment forecasts. These
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A Summary of the transportation programs and provisions of the
CAAA has been written by the Federal Highway Administration
(FHWA, 1992a). Summaries both of the complete ISTEA, and of
its air quality programs and provisions are also provided by
the Federal Highway Administration (FHWA, 1992,c).
21
Click HERE for graphic.
22
forecasts are suitably disaggregated by household type or
economic sector based on significantly different observed
averaged trip rates. The trip generation (and trip attraction)
models are usually regression based, or built on category
analytic techniques (see Douglas and Lewis, 1970/71; Institute
of Traffic Engineers, 1987).
For a set of zone-specific, average daily trip originations,
one or more trip distribution models are then used to allocate
purpose-specific trips to destinations within the remaining set
of urban analysis zones. Within these spatial interactionmodels the concept of locational accessibility to opportunities
plays a central role in the allocation process. If travel is a
derived demand, then accessibility is the "good" it provides.
Such locational accessibility indices have the form
Click HERE for graphic.
The use of spatial interaction models in urban planning
studies gained a boost with the elaboration of both entropy
maximizing (Wilson, 1967, 1970) and utility maximizing
(Neidercorn and Bechdolt, 1969; Golob, Gustafson and
Beckmann, 1973) theories, which have provided, respectively, amore robust statistical mechanics/ information theoretic basis
and a rational economic basis for spatial interaction theory.
Subsequent theoretical efforts to link these two approaches
during the 1970s and 1980s have further strengthened the hold of
"logit"forms of interaction model on the discipline (see Anas,
1983a; Brotchie et al., 1979; Williams, 1977; Wilson, et al.,
1981). Such a logit model can be stated as
Click HERE for graphic.
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This is a popular form of origin, demand or "production"
constrained spatial interaction model, which Wilson (1971)
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placed within a family of possible models, including destination
(supply, Aattraction@)-constrained as well as demand and supply
(Adoubly@)-constrained forms. The issues of why and when we
travel are handled within this framework by incorporating
disaggregations by trip purpose and time of day, respectively.
This usually leads to separate matrices of zone-to-zone flows
coming out of a work trip model and one or more types of
nonwork trip (e.g., shopping, social and recreational, school
trip) distribution models.
The modal choice submodel Asplits@ these interzonal trip
volumes across the most likely travel modes (usually auto versus
public rail or bus transit, but with walk, cycle or multimodal
trips also possible). The logit is again the most popular form
in use. At this step Adisaggregate@Cthat is, individual
traveler Cresponse-based multinomial logit models have also
become popular in the United States, using McFadden's (1974)
maximum likelihood method to include a wide range of
explanatory variables as well as multiple travel choices within
such model calibration efforts.
A subsequent and now increasingly used theoretical
development was the specification of "nested" logit forms, which
allow the results from one production-or attraction-constrainedlogit model to be passed into another in a behaviorally
consistent manner (see Williams, 1977; Ben-Akiva and Lerman,
1985). For example, mode m-specific travel (dis)utilities,
cijm (i.e., modal travel costs), can be averaged into a
destination choice model such as Eq. (2) above, using log-sum
or inclusive value terms of the form
Click HERE for graphic.
Click HERE for graphic.
Doubly constrained spatial interaction models have been popularas journey-to-work models where a planning agency has census
data or other means of producing what it considers reasonably
accurate estimates of zonally based trip productions and
attractions.
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which, in terms of equation (1) above is a log-accessibility
measure, and which in economic terms is often interpreted as a
locational or consumer=s surplus measure associated with zone i
(see Williams, 1977; Fisk and Boyce, 1984).
The resulting mode-specific interzonal traffic volumes are
then assigned to one or more routes, or paths, by the traffic
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assignment submodel shown in Fig. 3. This results in a new set
of interzonal travel costs which ought to be submitted back to
the trip distribution model. The process of model calibration
should then be continued by iterating the travel costs within
the various mode, destination, and assignment submodels until
they converge to a single set of values.
A number of variants on this iterative procedure are now
used (see Boyce, Lupa, and Zhang, 1994). At the traffic
assignment stage the auto trips and any truck trip matrices that
have been generated are converted into passenger car equivalent
traffic volumes before being simultaneously loaded onto the
highway network. Logits can also be used to select alternative
routes and have been incorporated within a number of different
assignment methods (see Sheffi, 1985). However, the most
commonly referenced assignment model is the capacity-sensitive
approach proposed by Wardrop (1952). Under this approach,
which is geared to handling the congested conditions experienced
during the commute to and from work, urban traffic volumes are
distributed such that all multilink routes used between any
origin- to-destination pair of traffic zones have the same
travel time, while all available but unused routes have a higher
travel time. The result is termed a user optimal equilibrium
assignment in which no traveler can change his or her routewithout incurring extra en route delays (Beckmann, McGuire, and
Winsten, 1956). Mathematically, this can be stated as
subject to
Click HERE for graphic.
where Tij is a trip matrix, and we are solving for fa = the flow
of traffic on link a. Here Ca(fa) = the congestion-sensitive
cost of travel along link a, such as a convex function of
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Click HERE for graphic.
Figure 4 shows a simple two-route, two-link example for
the type of link speed-volume relationships often used in
practice. The area created under these two marginal link
travel cost curves is the solution to the objective function
given by Eq. (6) above. Efficient computational procedures now
exist for solving this and similar capacity-constrained traffic
assignment problems for quite large and detailed urban area
networks. Recent developments by Janson (1991) and Janson and
Southworth (1992) have also extended this sort of equilibrium
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assignment model into computationally tractable dynamic forms,
which may soon allow the analysis of such strategies as
staggered work trip departure times and their effects on traffic
congestion. Such developments also take us squarely into the
realm of Intelligent Transportation Systems (ITS) research, an
area currently receiving large amounts of funding from the U.S.
Department of Transportation (DOT) in support of the 1991
ISTEA legislation.
Rail transit options are usually modeled over their own,
separate network. Where bus transit is a significant
alternative, passenger car equivalent (pce) conversion factors
can be used to simulate the effects of each bus within the
resulting traffic stream, and suitable network coding techniques
can handle the presence of bus-only lanes or other forms of
high-occupancy vehicle (HOV) facility. A similar pce procedure
can also be used to portray the effects of larger trucks in the
traffic stream.
Variants on this same four-step transportation system
modeling process are often used for both long-term (10-to 30-
year) planning, and shorter range (1-to 5-year) transportation
system management (TSM) planning (see Yu, 1982, for an
overview). In some cases specifically designed variants on theoverall modeling approach have been developed to better focus
on a particular TSM strategy; these include the Network
Performance Evaluation Model developed for the U.S.
Department of Energy (DOE) to analyze the energy and
environmental impacts of various types of HOV lanes, (see
Janson, Zozaya-Gorostiza, and Southworth, 1987).
Fuel use and related mobile-source, pollutant-specific
emissions estimates are typically computed using these
assignment model-generated traffic volumes and speeds. For this
purpose baseline emissions estimates for light-duty motor
vehicles (automobiles and light trucks) are generated by the
Federal Test Procedure. Under the FTP vehicles go
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Click HERE for graphic.
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through a series of stops and starts with an average driving
speed of 19.6 mph. Emissions rates for vehicles at other speeds
are derived by a statistical regression of fuel consumption
against average speedfor cycles other than the FTP. Speed
correction factors (SCFs) for this purpose have been developed
by the U.S. Environmental Protection Agency and by theCalifornia Department of Transportation.
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However, these emissions outputs, and the traffic
volumes themselves, are usually aggregated or averaged over one
or more traffic analysis zones for the purposes of computing
emissions on a wider regional or Agridded@ basis (see Quint and
Loudon, 1994; Outwater and Loudon, 1994). Currently, there is
a good deal of uncertainty surrounding the accuracy of
emissions calculations for carbon monoxide (CO), hydrocarbons
(HC), and oxides of nitrogen (NOX) and, in particular, their
relationship to actual traffic conditions (see Guensler, 1993;
Bae, 1993). Nor were the traffic volumes and speeds from static
traffic assignment models meant to handle such details. While
detailed traffic simulation programs based on individual vehicle
movements are now also in use, it has been only recently, and in
a research context, that this sort of detailed traffic flow
modeling has been tied directly to emissions estimation (see
Matzoros and Van Vliet, 1992a,b), and little testing of its
accuracy has been carried out.
For the purposes of estimating areawide CO2 emissions,
which are highly correlated with total fuel used, less concern
for such accuracy may be warranted. Unlike the CAAA-controlled
pollutants, which are by volume comparatively marginal engine
emissions, CO2 emissions are highly correlated with fuel used
and associated, congestion-conditioned vehicle miles traveled(VMT). Nor need we be concerned within such an analysis of
greenhouse gas buildup with such location-specific issues as the
health effects of CO hotspots.
2.4 LINKING TRANSPORTATION AND URBAN LAND
USE MODELS
Urban land use modeling also began in the 1950s, again in the
United States (see Batty, 1980, who dates such efforts from
1958). Most of today=s operational land use-transportation
models derive from ideas and model forms introduced into the
wider literature during the 1960s and 1970s. There is now an
extensive literature dealing with the theoretical and
methodological as well as operational aspects of such models.
The discussion presented below draws on the historical and
technical accounts and efforts at synthesis described in, among
others, Anas (1984), Batty and Hutchinson (1983), Berechman
and Gordon (1986), Berechman and Small (1988), Bertuglia et
al. (1987), Echenique and Williams (1980), Echenique (1985),
Kim (1989), MacGill and Wilson (1979), Mackett (1985, 1994),
Putman (1983, 1991, 1994), Transportation Research Board
(1990), Wegener (1994, 1995b), Wilson (1987), and Wilson et al.
(1977, 1981).
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Figure 5 shows the basic idea behind linking a land use
model to the four-step transportation planning model described
above. As noted in this figure, a number of modeling systems
use the spatial interaction formulas at the heart of their
residential and employment location submodels to replace
(obviate the need for) a separate set of trip-based distribution
models. The ITLUP model can be used to generate such trip
distributions within the DRAM submodel. The MEPLAN and TRANUS
models generate all of their inter-zonal flow matrices as a
series of Atrades@ within the land use modeling system. Within
a number of operational models, including the MEPLAN and Kim
models described later in this review, the urban system is
modeled as a series of markets, with emphasis placed on clearing
a transportation market and one or more other land use markets,
by solving endogenously for a suitable set of spatially varying
market prices; which include travel costs and site rents.
Within the less inclusive models, such as ITLUP, which avoid
endogenous modeling of nontransportation price mechanisms, an
equilibrium between the transportation system=s demands and
supplies can also be brought about; this also stabilizes the
parameters within the residential and employment activity
location submodels. Such considerations of equilibrium in urban
evolution quickly take us into the area of temporal dynamics.Within the ITLUP, MEPLAN, and Dortmund models described
in some detail below, lagged effects play an important role in
linking different submodels within the transportation and land
use systems both across as well as within a single time period
(see Sect. 2.5, below).
While operational transportation planning models have
tended to be built around the above four-step approach, once we
link these developments to urban land use models a good deal
more variety is evident. At least five significantly different
theoretical and/or methodological approaches have combined to
produce the current state of best practice among such extended
and Aintegrated@ modeling systems. Each of these
approachesCthe Lowry model, normative and mathematical
programming developments, spatial input-output analysis, urban
economics, and microanalytical simulationCis reviewed briefly
below.
In the discussion of each of these approaches a model
from Table 2 has been selected for detailed presentation, as a
means of demonstrating how such developments translate into
current modeling practice. The reader should note, however,
that the assignment of a model below to a particular approach is
somewhat arbitrary. The order of presentation was selected to
show how current models have brought developments from anumber of the above discussed advances into their frameworks.
A significant feature of model advances over the past 30 years
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has been the gradual incorporation and unification of different
theories and methods within individual modeling frameworks. The
purpose of the following descriptions is not to fully elaborate
on any single modeling system but to use specific models to
elaborate on key areas of development. In selecting examples for
presentation there is also a strong bias towards U.S.-based
modeling efforts. For a complete list of a model's current
functionality the reader should see the references cited in the text.
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2.4.1 The Lowry Model and Related Developments
2.4.1.1 Background
Most operational urban land use models today, and all of
those discussed below, can trace their beginnings to Lowry=s
(1964) AModel of Metropolis@ for the city of Pittsburgh. The
original Lowry model incorporates the spatial distribution of
population, employment, retailing (the entire service, or "non-basic," sector), and land use within a compact iterative
procedure requiring only nine equations and three inequalities.
In essence, the approach consists of linking together two
spatial interaction models. One of these models allocates
workers to a predefined set of land use zones on the basis of
exogenously supplied basic employment levels (i.e., employment
in manufacturing and primary industries). The dependent
families of these workers are then defined using a suitable
activity ratio (the ratio of total regional population to total
regional employment). These workers and their families demand
services, and these demands are met by means of a second spatial
interaction model which allocates this service supply, in the
form of "nonbasic" employment, across the same spatial zoning
system. Iteration is required to then bring the resulting
residential and nonbasic employment activity allocation models
into line with each other. To generate estimates of either land
area occupied or floor space used